Data-Driven Distributed Optical Vibration Sensors: A Review

Distributed optical vibration sensors (DOVS) have attracted much attention recently since it can be used to monitor mechanical vibrations or acoustic waves with long reach and high sensitivity. Phase-sensitive optical time domain reflectometry (Φ-OTDR) is one of the most commonly used DOVS schemes. For Φ-OTDR, the whole length of fiber under test (FUT) works as the sensing instrument and continuously generates sensing data during measurement. Researchers have made great efforts to try to extract external intrusions from the redundant data. High signal-to-noise ratio (SNR) is necessary in order to accurately locate and identify external intrusions in Φ-OTDR systems. Improvement in SNR is normally limited by the properties of light source, photodetector and FUT. But this limitation can also be overcome by post-processing of the received optical signals. In this context, detailed methodologies of SNR enhancement post-processing algorithms in Φ-OTDR systems have been described in this paper. Furthermore, after successfully locating the external vibrations, it is also important to identify the types of source of the vibrations. Pattern classification is a powerful tool in recognizing the intrusion types from the vibration signals in practical applications. Recent reports of Φ-OTDR systems employed with pattern classification algorithms are subsequently reviewed and discussed. This thorough review will provide a design pathway for improving the performance of Φ-OTDR while maintaining the cost of the system as no additional hardware is required

Distributed Fiber Ultrasonic Sensor and Pattern Recognition Analytics

Ultrasound interrogation and structural health monitoring technologies have found a wide array of applications in the health care, aerospace, automobile, and energy sectors. To achieve high spatial resolution, large array electrical transducers have been used in these applications to harness sufficient data for both monitoring and diagnoses. Electronic-based sensors have been the standard technology for ultrasonic detection, which are often expensive and cumbersome for use in large scale deployments. Fiber optical sensors have advantageous characteristics of smaller cross-sectional area, humidity-resistance, immunity to electromagnetic interference, as well as compatibility with telemetry and telecommunications applications, which make them attractive alternatives for use as ultrasonic sensors. A unique trait of fiber sensors is its ability to perform distributed acoustic measurements to achieve high spatial resolution detection using a single fiber. Using ultrafast laser direct-writing techniques, nano-reflectors can be induced inside fiber cores to drastically improve the signal-to-noise ratio of distributed fiber sensors. This dissertation explores the applications of laser-fabricated nano-reflectors in optical fiber cores for both multi-point intrinsic Fabry–Perot (FP) interferometer sensors and a distributed phase-sensitive optical time-domain reflectometry (φ-OTDR) to be used in ultrasound detection. Multi-point intrinsic FP interferometer was based on swept-frequency interferometry with optoelectronic phase-locked loop that interrogated cascaded FP cavities to obtain ultrasound patterns. The ultrasound was demodulated through reassigned short time Fourier transform incorporating with maximum-energy ridges tracking. With tens of centimeters cavity length, this approach achieved 20kHz ultrasound detection that was finesse-insensitive, noise-free, high-sensitivity and multiplex-scalability. The use of φ-OTDR with enhanced Rayleigh backscattering compensated the deficiencies of low inherent signal-to-noise ratio (SNR). The dynamic strain between two adjacent nano-reflectors was extracted by using 3×3 coupler demodulation within Michelson interferometer. With an improvement of over 35 dB SNR, this was adequate for the recognition of the subtle differences in signals, such as footstep of human locomotion and abnormal acoustic echoes from pipeline corrosion. With the help of artificial intelligence in pattern recognition, high accuracy of events’ identification can be achieved in perimeter security and structural health monitoring, with further potential that can be harnessed using unsurprised learning

Microwave reflectometric systems and monitoring apparatus for diffused-sensing applications

Most sensing networks rely on punctual/local sensors; they thus lack the ability to spatially resolve the quantity to be monitored (e.g. a temperature or humidity profile) without relying on the deployment of numerous inline sensors. Currently, most quasi-distributed or distributed sensing technologies rely on the use of optical fibre systems. However, these are generally expensive, which limits their large-scale adoption. Recently, elongated sensing elements have been successfully used with time-domain reflectometry (TDR) to implement diffused monitoring solutions. The advantage of TDR is that it is a relatively low-cost technology, with adequate measurement accuracy and the potential to be customised to suit the specific needs of different application contexts in the 4.0 era. Based on these considerations, this paper addresses the design, implementation and experimental validation of a novel generation of elongated sensing element networks, which can be permanently installed in the systems that need to be monitored and used for obtaining the diffused profile of the quantity to be monitored. Three applications are considered as case studies: monitoring the irrigation process in agriculture, leak detection in underground pipes and the monitoring of building structures

Development of a distributed optical fiber sensor for geological applications

The purpose of the study was to develop a distributed optical fiber acoustic sensor for monitoring ground subsidence before collapse sinkholes form causing costly damage on infrastructure. Costs in excess of R1.3 billion have been incurred while dealing with sinkhole related measures in South Africa. Monitoring sinkholes and the presence of an early warning alert system can drastically reduce the impact, risk and cost caused by sudden ground collapse. A related goal was to construct a reliable collapse alert early warning system to facilitate disaster preparedness and avoid further damage from accidents. This was achieved by developing a spectroscopic shift monitoring algorithm which analysed changes in the subsurface vibration modes using ambient noise signals. For the first time to our knowledge, an optic fiber sensor with an early warning alarm, using ambient noise vibrations to detect and monitor sinkholes was developed at NMU. A polarisation-based, interferometric optical fiber seismic sensor was developed and compared to a commercial geophone. The fiber sensor exhibited superior performance in sensitivity, bandwidth, signal response and recovery times. The sensitivity of the optical fiber sensor was 0.47 rad/Pa surpassing the geophone sensitivity by 9.32%, and the bandwidth of 3.349kHz was 20 times greater for the optical fiber sensor. The fiber sensor was used to measure millisecond events as the impact duration of a bouncing ball was successfully obtained. It was used to detect sinkhole formation in the simulator model, designed. Ground collapse precursors were identified, and early warning alert was achieved using the spectral analysis algorithm, developed. The collapse precursor condition was identified as a functional combination of variations in the peak frequency, bandwidth and peak intensity. A distributed acoustic sensor was built to detect ambient noise induced subsurface signals. Vibrations were located along the 28km length of optical fiber with a relative error of 9.6%. The sensor demonstrated a frequency response range of 212.25Hz, an event distance precision of 224m with time resolution of 1.12µs, and a spatial resolution of 1km. The position of disturbance was measured within 300m of its actual point of 3.21km along the optical fiber. The results showed that distributed optical fiber sensing allows real-time monitoring of the subsurface over extended distances, using ambient noise signals.Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202

Development of a distributed optical fiber sensor for geological applications

The purpose of the study was to develop a distributed optical fiber acoustic sensor for monitoring ground subsidence before collapse sinkholes form causing costly damage on infrastructure. Costs in excess of R1.3 billion have been incurred while dealing with sinkhole related measures in South Africa. Monitoring sinkholes and the presence of an early warning alert system can drastically reduce the impact, risk and cost caused by sudden ground collapse. A related goal was to construct a reliable collapse alert early warning system to facilitate disaster preparedness and avoid further damage from accidents. This was achieved by developing a spectroscopic shift monitoring algorithm which analysed changes in the subsurface vibration modes using ambient noise signals. For the first time to our knowledge, an optic fiber sensor with an early warning alarm, using ambient noise vibrations to detect and monitor sinkholes was developed at NMU. A polarisation-based, interferometric optical fiber seismic sensor was developed and compared to a commercial geophone. The fiber sensor exhibited superior performance in sensitivity, bandwidth, signal response and recovery times. The sensitivity of the optical fiber sensor was 0.47 rad/Pa surpassing the geophone sensitivity by 9.32%, and the bandwidth of 3.349kHz was 20 times greater for the optical fiber sensor. The fiber sensor was used to measure millisecond events as the impact duration of a bouncing ball was successfully obtained. It was used to detect sinkhole formation in the simulator model, designed. Ground collapse precursors were identified, and early warning alert was achieved using the spectral analysis algorithm, developed. The collapse precursor condition was identified as a functional combination of variations in the peak frequency, bandwidth and peak intensity. A distributed acoustic sensor was built to detect ambient noise induced subsurface signals. Vibrations were located along the 28km length of optical fiber with a relative error of 9.6%. The sensor demonstrated a frequency response range of 212.25Hz, an event distance precision of 224m with time resolution of 1.12µs, and a spatial resolution of 1km. The position of disturbance was measured within 300m of its actual point of 3.21km along the optical fiber. The results showed that distributed optical fiber sensing allows real-time monitoring of the subsurface over extended distances, using ambient noise signals.Thesis (PhD) -- Faculty of Science, School of Computer Science, Mathematics, Physics and Statistics, 202

Intensity based interrogation of optical fibre sensors for industrial automation and intrusion detection systems

In this study, the use of optical fibre sensors for intrusion detection and industrial automation systems has been demonstrated, with a particular focus on low cost, intensity-based, interrogation techniques. The use of optical fibre sensors for intrusion detection systems to secure residential, commercial, and industrial premises against potential security breaches has been extensively reviewed in this thesis. Fibre Bragg grating (FBG) sensing is one form of optical fibre sensing that has been underutilised in applications such as in-ground, in-fence, and window and door monitoring, and addressing that opportunity has been a major goal of this thesis. Both security and industrial sensor systems must include some centralised intelligence (electronic controller) and ideally both automation and security sensor systems would be controlled and monitored by the same centralised system. Optical fibre sensor systems that could be used for either application have been designed, developed, and tested in this study, and optoelectronic interfaces for integrating these sensors with electronic controllers have been demonstrated. The versatility of FBG sensors means that they are also ideal for certain mainstream industrial applications. Two novel transducers have been developed in this work; a highly sensitive low pressure FBG diaphragm transducer and a FBG load cell transducer. Both have been designed to allow interrogation of the optical signal could occur within the housing of the individual sensors themselves. This is achieved in a simple and low cost manner that enables the output of the transducers to be easily connected to standard electronic controllers, such as programmable logic controllers. Furthermore, some of the nonlinear characteristics of FBG sensors have been explored with the aim of developing transducers that are inherently decoupled from strain and temperature interference. One of the major advantages of optical fibre sensors is their ability to be both time division and wavelength division multiplexed. The intensity-based interrogation techniques used here complement this attribute and are a major consideration when developing the transducers and optoelectronic circuits. A time division multiplexing technique, using transmit-reflect detection and incorporating a dual bus, has also been developed. This system architecture enables all the different optical fibre transducers on the network to have the same Bragg wavelength and hence the number of spare replacement transducers required is minimal. Moreover, sensors can be replaced in an online control system without disrupting the network. In addition, by analysing both the transmitted and reflected signals, problems associated with optical power fluctuations are eliminated and the intensity of the sensor signals is increased through differential amplification. Overall, the research addresses the limitations of conventional electrical sensors, such as susceptibility to corrosive damage in wet and corrosive environments, and risk of causing an explosion in hazardous environments, as well as the limitations of current stand-alone optical fibre sensor systems. This thesis supports more alert, reliable, affordable, and coordinated, control and monitoring systems in an on-line environment

A review of automated solar photovoltaic defect detection systems : approaches, challenges, and future orientations

The development of Photovoltaic (PV) technology has paved the path to the exponential growth of solar cell deployment worldwide. Nevertheless, the energy efficiency of solar cells is often limited by resulting defects that can reduce their performance and lifespan. Therefore, it is crucial to identify a set of defect detection approaches for predictive maintenance and condition monitoring of PV modules. This paper presents a comprehensive review of different data analysis methods for defect detection of PV systems with a high categorisation granularity in terms of types and approaches for each technique. Such approaches, introduced in the literature, were categorised into Imaging-Based Techniques (IBTs) and Electrical Testing Techniques (ETTs). Although several review papers have investigated recent solar cell defect detection techniques, they do not provide a comprehensive investigation including IBTs and ETTs with a greater granularity of the different types of each for PV defect detection systems. Types of IBTs were categorised into Infrared Thermography (IRT), Electroluminescence (EL) imaging, and Light Beam Induced Current (LBIC). On the other hand, ETTs were categorised into Current-Voltage (I-V) characteristics analysis, Earth Capacitance Measurements (ECM), Time Domain Reflectometry (TDR), Power Losses Analysis (PLA), and Voltage and Current Measurements (VCM). Approaches based on digital/signal processing and Machine Learning (ML) models for each method are included where relevant. Moreover, the paper critically analyses the advantages and disadvantages of each of the adopted techniques, which can be referred to by future studies to identify the most suitable method considering the use-case’s requirements and setting. The adoption of each of the reviewed techniques depends on several factors, including the deployment scale, the targeted defects for detection, and the required location of defect analysis in the PV system, which are expanded further in the presented analysis. From a high-level perspective, while IBTs provide a high-resolution visual representation of the module surface, allowing for the detection and diagnosis of small structural defects that may be missed by other techniques, ETTs can detect electrical faults beyond the PV module’s surface. On the IBT level, the most notable adopted techniques in the literature are IRT- and EL-based. While IRT techniques are more practical for large-scale applications than EL imaging, the latter is considered a non-intrusive technique that is highly efficient in localising defects of solar cells. The paper also discusses challenges observed in the state-of-the-art related to data availability, real-time monitoring, accurate measurements, computational efficiency, and dataset distribution, and reviews data pre-processing and augmentation approaches that can address some of these challenges. Furthermore, potential future orientations are identified, addressing the limitations of PV defect detection systems